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Sensor Networks to Monitor ElderlySensor Networks to Monitor Elderly

Yusuf AlbayramComputer Science & EngineeringUniversity of Connecticut, Storrs

yusuf.albayram@uconn.edu

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IntroductionIntroduction The proportion of elderly in the world is The proportion of elderly in the world is

demonstrating a remarkable increase every year.demonstrating a remarkable increase every year. By the year 2050, 1 in 5 person in the world will

be age 60 or older, 1.6 million people in the aging population live in 1.6 million people in the aging population live in

facilities facilities Typical residents need assistance with 2 activities of Typical residents need assistance with 2 activities of

daily living daily living

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ProblemsProblems With the increase of elderly people population:With the increase of elderly people population:

Rising Health Care Costs More investment is needed for elderly care

Many elderly people choose to stay at home e.g., Due to privacy/dignity issues.

A majority of older adults are challenged by chronic and acute illnesses and/or injuries. 80% of older Americans have one or more chronic

diseases. The growing insufficiency of traditional family

care i.e., decreased care by relatives

Decrease in the working population will cause a shortage of skilled caregivers.

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State of the art applicationsState of the art applications Advances in sensor technology, object localization, Advances in sensor technology, object localization,

wireless communications technologies canwireless communications technologies can enable elderly people to regain their capability of

independent living make possible unobtrusive supervision of basic

needs of frail elderly and thereby replicate services of on-site health care providers

Assisted Living Technologies are expected to Assisted Living Technologies are expected to contribute significantlycontribute significantly improving the quality of life of elders reducing costs by avoiding premature

institutionalization

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What services can assisted living systems offer? What services can assisted living systems offer? Alarms/notifications and triggers Alarms/notifications and triggers Queries Queries Reminders Reminders Detect anomalies and deviations Detect anomalies and deviations Recognize specific behaviors and assist with task Recognize specific behaviors and assist with task

completion completion Keep the person active and connected to the social Keep the person active and connected to the social

environment environment

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OverviewOverview Introduction & MotivationIntroduction & Motivation

Sensor Networks to Monitor ElderlySensor Networks to Monitor Elderly (1) Activities of Daily Living Monitoring, (2) Location Tracking, (3) Medication Intake Monitoring, (4) Medical Status Monitoring, (5) Fall and Movement Detection

ChallengesChallenges

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(1.1) Activities of Daily Living Monitoring(1.1) Activities of Daily Living Monitoring Monitoring the patient’s activities of daily living Monitoring the patient’s activities of daily living

(ADLs) is essential to (ADLs) is essential to Detects anomalies and prompts them, Assist the independent living of older adults The diagnosis of diseases and health problems

Several projects have investigated the use of pervasive Several projects have investigated the use of pervasive sensors to provide a ‘smart’ environment for the sensors to provide a ‘smart’ environment for the observation of (ADL)observation of (ADL) The use of heterogeneous sensors, including

Wearable sensors (Body Sensor Network (BSN))– Designed to collect biomedical, physiological and activity data

Ambient sensors (Ambient Sensor Network (ASN))– Designed to collect data around the region where the ADL

takes place.

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(1.2) Activities of Daily Living Monitoring(1.2) Activities of Daily Living Monitoring Variety of multi-modal and unobtrusive wireless Variety of multi-modal and unobtrusive wireless

sensors seamlessly integrated into ambient-sensors seamlessly integrated into ambient-intelligence compliant objects (AICOs) to achieve intelligence compliant objects (AICOs) to achieve activity recognitionactivity recognition

[17] Overview of assisted living populated with a variety of wireless multimodal sensors to collect data for various ADLs

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(2) Location Tracking(2) Location Tracking 25% of people over 60+ suffer from Alzheimer’s and 25% of people over 60+ suffer from Alzheimer’s and

Dementia Dementia Seniors with Dementia or Alzheimer’s can easily Seniors with Dementia or Alzheimer’s can easily

become confused or lost.become confused or lost. Monitoring location of a person suffering dementia or Monitoring location of a person suffering dementia or

Alzheimer’s can help Alzheimer’s can help Detect signs of disorientation or wandering. The health professional to reach a diagnosis of a

type of dementia. Several methods for location tracking have been Several methods for location tracking have been

proposed:proposed: (1) GPSs based outdoor location tracking (2) RFID-based indoor location tracking IR, ultrasound

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(2.1) Location Tracking(2.1) Location Tracking (1) GPSs based outdoor location tracking(1) GPSs based outdoor location tracking

GPS-enabled devices include an SOS button and once pressed , connect with their family member or caregiver. 

GPS Tracker Bracelets

Smart Phone with GPS

Wearable AGPS terminal

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(2.2) Location Tracking(2.2) Location Tracking (2) RFID-based indoor location tracking(2) RFID-based indoor location tracking GPS does not work in indoorGPS does not work in indoor Real-time monitoring of elderly people’s whereaboutsReal-time monitoring of elderly people’s whereabouts

The movement of the elderly person wearing an RFID tag is sensed by the RFID readers installed in the building

The RFID-based location sensing system in smart home environments

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(2.3) Location Tracking(2.3) Location Tracking Critique for location tracking systems Critique for location tracking systems

Privacy is one of the major issue Too battery-hungry and battery drain quickly

(e.g., smart phones) Devices must be lightweight, small, and

comfortable to wear and use Elders often have no idea using computers,

smartphones and other technological tools their interaction with them must be simple And limited to a minimum

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(3) Medication Intake Monitoring(3) Medication Intake Monitoring Taking medications is one of the most important Taking medications is one of the most important

activities in an elder’s daily lifeactivities in an elder’s daily life Elders taking on average of about 5.7 prescription

medicines and 4 nonprescription drugs each day [15] Medication intake monitoring is essentialMedication intake monitoring is essential

Medication noncompliance is common in elderly and chronically ill especially when cognitive disabilities are encountered [13].

The existing methods/systems often utilize following sensor The existing methods/systems often utilize following sensor technologies for medication intake monitoring :technologies for medication intake monitoring : RFID Computer vision

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(3.1) Medication Intake Monitoring(3.1) Medication Intake Monitoring Integrating both sensor network and RFID technologiesIntegrating both sensor network and RFID technologies

HF RFID tags to identify when and which bottle is removed or replaced by the patient

The weight scale monitors the amount medicine on the scale The patient wearing an Ultra High Frequency (UHF) RFID

tag is determined in the vicinity and alert the patient to take the necessary medicines.

Medicine Monitor System Prototype

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(3.2) Medication Intake Monitoring(3.2) Medication Intake Monitoring Incorporating RFID and video analysis [10]Incorporating RFID and video analysis [10]

RFID tags applied on medicine bottles located in a medicine cabinet and RFID readers detect if any of these bottles are taken away

A video camera monitoring the activity of taking medicine by integrating face and mouth detection

Monitoring the activity of taking medications using computer vision-based method

RFID system includes antenna and RFID reader

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(4) Medical Status Monitoring(4) Medical Status Monitoring Health monitoring devices are primary responsible forHealth monitoring devices are primary responsible for

Collecting physiological data from the patient (e.g., ECG, heart rate, blood pressure)

Transmitting them securely to a remote site for further evaluation

At the health provider’s end, At the health provider’s end, the medical personnel and supervising physicians

can have instant access to real-time physiological measurements the medical history of several monitored patients

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(4.1) Medical Status Monitoring(4.1) Medical Status Monitoring

The health monitoring network structure [16]

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(5) Fall and Movement Detection(5) Fall and Movement Detection Fall Events very common situation in elderly peopleFall Events very common situation in elderly people

30% of the older persons fall at least once a year Fall responsible of 70% of accidental death in

persons aged 75+ There are primarily 3 types of fall detection methods There are primarily 3 types of fall detection methods

for elderly for elderly (1) Wearable device based methods (2) Vision based methods (3) Ambient based methods

Once the fall event was detected, an alert email is Once the fall event was detected, an alert email is immediately sent to the caregiverimmediately sent to the caregiver

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(5.1) Fall and Movement Detection(5.1) Fall and Movement Detection (1) Wearable device based methods(1) Wearable device based methods

Using accelerometers and gyroscopes to analyze changes in a body’s position to detect falls.

A tri-axial accelerometer for monitoring accelerationand a tri-axial gyroscope for monitoring angular velocity [14]

the sensor nodes are attachedon the chest (Node A) and thigh (Node B)

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(5.2) Fall and Movement Detection(5.2) Fall and Movement Detection (2) Vision based methods(2) Vision based methods

Detect Fall from a video sequence by: Applying background subtraction to extract the

foreground human body and post processing to improve the result [2,3]

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(5.3) Fall and Movement Detection(5.3) Fall and Movement Detection (3) Ambient based methods(3) Ambient based methods

Rely on pressure sensors, acoustic sensors or even passive infrared motion sensors, which are usually implemented around caretakers’ houses

Once the fall event was detected, an alert call/email Once the fall event was detected, an alert call/email was immediately sent.was immediately sent.

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(5.4) Fall and Movement Detection(5.4) Fall and Movement Detection Critique for automatic fall detection, Critique for automatic fall detection,

(+) Video based methods are usually more accurate (-) Video based methods raise privacy concerns (+) Acoustics based methods are very susceptible

to ambient noise (-) Video-based and acoustic-based methods are

costly due to pre-installation (-) Wearable based methods operate as long as the

person wears the sensors (+) With the improvements in smart phone tech

(built-in sensors e.g., accelerometer, gyroscope), Smart phones are ideal for developing an app that can automatically detect falls and provide a warning mechanism.

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Challenges of Sensor Networks solutions for monitoring Challenges of Sensor Networks solutions for monitoring elderlyelderly

Hardware level challengesHardware level challenges Unobtrusiveness Sensitivity and calibration Energy Data acquisition efficiency

SecuritySecurity PrivacyPrivacy User-friendlinessUser-friendliness Ease of deployment and scalabilityEase of deployment and scalability MobilityMobility

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ReferencesReferences[1] Wang, J., et al. "An enhanced fall detection system for elderly person monitoring using consumer [1] Wang, J., et al. "An enhanced fall detection system for elderly person monitoring using consumer home networks." Consumer Electronics, IEEE Transactions on 60.1 (2014): 23-29.home networks." Consumer Electronics, IEEE Transactions on 60.1 (2014): 23-29.[2] Yu, Miao, et al. "A posture recognition-based fall detection system for monitoring an elderly [2] Yu, Miao, et al. "A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment." Information Technology in Biomedicine, IEEE Transactions on person in a smart home environment." Information Technology in Biomedicine, IEEE Transactions on 16.6 (2012): 1274-1286.16.6 (2012): 1274-1286.[3] Foroughi, Homa, Baharak Shakeri Aski, and Hamidreza Pourreza. "Intelligent video surveillance [3] Foroughi, Homa, Baharak Shakeri Aski, and Hamidreza Pourreza. "Intelligent video surveillance for monitoring fall detection of elderly in home environments." Computer and Information for monitoring fall detection of elderly in home environments." Computer and Information Technology, 2008. ICCIT 2008. 11th International Conference on. IEEE, 2008.Technology, 2008. ICCIT 2008. 11th International Conference on. IEEE, 2008.[4] Yavuz, Gokhan, et al. "A smartphone based fall detector with online location support." [4] Yavuz, Gokhan, et al. "A smartphone based fall detector with online location support." International Workshop on Sensing for App Phones; Zurich, Switzerland. 2010.International Workshop on Sensing for App Phones; Zurich, Switzerland. 2010.[5] Popescu, Mihail, et al. "An acoustic fall detector system that uses sound height information to [5] Popescu, Mihail, et al. "An acoustic fall detector system that uses sound height information to reduce the false alarm rate." Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th reduce the false alarm rate." Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE. IEEE, 2008.Annual International Conference of the IEEE. IEEE, 2008.[6] Huang, Kung-Ta, et al. "An intelligent RFID system for improving elderly daily life independent [6] Huang, Kung-Ta, et al. "An intelligent RFID system for improving elderly daily life independent in indoor environment." Smart homes and health telematics. Springer Berlin Heidelberg, 2008. 1-8.in indoor environment." Smart homes and health telematics. Springer Berlin Heidelberg, 2008. 1-8.[7] Ferreira, João. "Behavioral Analytics for Medical Decision Support: Supporting dementia [7] Ferreira, João. "Behavioral Analytics for Medical Decision Support: Supporting dementia diagnosis through outlier detection." (2012).diagnosis through outlier detection." (2012).[8] Wong, AK-S., et al. "An AGPS-based elderly tracking system." Ubiquitous and Future Networks, [8] Wong, AK-S., et al. "An AGPS-based elderly tracking system." Ubiquitous and Future Networks, 2009. ICUFN 2009. First International Conference on. IEEE, 2009.2009. ICUFN 2009. First International Conference on. IEEE, 2009.[9] Kim, Soo-Cheol, Young-Sik Jeong, and Sang-Oh Park. "RFID-based indoor location tracking to [9] Kim, Soo-Cheol, Young-Sik Jeong, and Sang-Oh Park. "RFID-based indoor location tracking to ensure the safety of the elderly in smart home environments." Personal and ubiquitous computing 17.8 ensure the safety of the elderly in smart home environments." Personal and ubiquitous computing 17.8 (2013): 1699-1707.(2013): 1699-1707.[10] Hasanuzzaman, Faiz M., et al. "Monitoring activity of taking medicine by incorporating RFID [10] Hasanuzzaman, Faiz M., et al. "Monitoring activity of taking medicine by incorporating RFID and video analysis." Network Modeling Analysis in Health Informatics and Bioinformatics 2.2 (2013): and video analysis." Network Modeling Analysis in Health Informatics and Bioinformatics 2.2 (2013): 61-70.61-70.

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